10 hours ago
Why Contextual AI Is Vital For The Future Of Supply Chains
Theodore Krantz, Chief Executive Officer at
There are two new norms. The first is AI. By 2028 alone, 33% of enterprise software applications will include agentic AI, and 15% of day-to-day work decisions will be autonomously decided for us. And the second norm to come to terms with is global supply chain risk. Our current landscape is challenging and involves complexity, volatility, extreme weather events, cybersecurity risks and geopolitical tensions.
For global enterprises, the question isn't simply whether to adopt AI. It's how to do so in a way that transforms fragmented systems into intelligent, adaptive ecosystems. That's where the distinction between applied and contextual AI becomes critical.
Let's start with what most people are familiar with: applied AI. These are targeted tools built to automate specific processes, forecasting demand, managing inventory and flagging compliance risks. They're great at solving isolated problems with structured inputs and predictable outputs. But applied AI has its limits. It doesn't always understand nuance, interconnected risks or the fast-moving variables that define today's global supply networks.
In contrast, contextual AI is much different. It does more than merely process data. It understands the environment in which that data lives. It can synthesize the real-time signals from a diverse array of sources, such as, but not limited to, cyber threats, regulatory updates, geopolitical developments and even financial stress indicators. In this way, contextual AI provides more meaningful and strategic recommendations, and this difference is centered on being proactive versus reactive.
Why Most AI Projects Fall Short
Despite the excitement surrounding generative AI and large language models, many companies remain stuck in what I call the AI adoption gap. A staggering statistic from shows that 90% of Fortune 1000 companies have little to no visibility into their second- and third-tier suppliers. They might use AI in pockets, but they lack the visibility and context to orchestrate decisions at scale to drive true retention and ROI. This is a massive gap, especially when most catastrophic risks tend to lie beyond your tier-one relationships.
So, why does this matter for AI? Because no matter how advanced your model is, it's only as effective as the data you feed it and the perspective you bring to interpreting that data. Good AI without context is like a GPS that doesn't update in real time: helpful but prone to failure when conditions shift.
The Case For Contextual AI In Supply Chains
What separates contextual AI is its ability to integrate internal enterprise data with external intelligence. It's important to take into consideration multiple data sources—some public, some proprietary—to map global supply chains and assess risk across multiple dimensions: cyber, financial, geopolitical, regulatory and ESG. And the real magic happens when we marry that external lens with customers' own data to mitigate risk proactively.
Take the recent outage involving CrowdStrike and Microsoft as an example. According to our data, about 675,000 direct customer relationships were impacted, and without a contextual view, understanding not just who was directly affected but how those ripple effects propagated across sub-tiers, organizations would be left guessing where their vulnerabilities truly lie.
Building Enterprise Trust In AI
Contextual AI is really about better outcomes. But to get there, enterprises need more than technology; they need trust. That trust starts with transparency, human-in-the-loop design and rigorous validation. Don't treat AI as a "set it and forget it" solution.
Companies must continuously refine their models with domain experts and customer feedback, integrating reinforcement learning with human validation. And increasingly, using clean rooms to ensure customers can safely combine their sensitive data with your company's market data without compromising privacy or compliance is vital.
AI in supply chains must meet a higher bar than many consumer-facing applications. Our business involves protecting livelihoods, handling geopolitical exposure and ensuring regulatory alignment across jurisdictions. That's why trust, explainability and governance are foundational, and this is true especially when AI is involved.
From Trial Projects To ROI-Based Business Transformation
The end goal isn't to bolt AI onto your existing workflows; it's to rethink the workflows themselves.
Applied AI may help you do what you already do more efficiently. Contextual AI helps you redefine what's possible. It allows you to spot weak signals before they become front-page headlines. Then, in turn, it adjusts in real time rather than after the fact and aligns decision-making across all sorts of systems, even fragmented ones.
This transformation will take time. And it will also require a shift in mindset as well as a retooling of infrastructure, but the results will be significant. It will mean more resilient, responsive and stronger supply chains that will benefit all over time.
As enterprise leaders, we must challenge ourselves not just to implement AI but to elevate it. And the real power of AI isn't in the code. It's in the context.
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